Table_8_Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.xlsx

BackgroundThe severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.
MethodsCOVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.
ResultsIn this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.
ConclusionIn this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

研究背景：新型冠状病毒肺炎（coronavirus disease 2019, COVID-19）是由严重急性呼吸综合征冠状病毒2型（severe acute respiratory syndrome coronavirus 2, SARS-CoV-2）引发的传染性疾病，其引发的大流行是现代史上最具毁灭性的公共卫生事件。人类免疫缺陷病毒（human immunodeficiency virus, HIV）可破坏免疫系统细胞，削弱机体抵御日常感染与疾病的能力。此外，HIV感染者的COVID-19死亡风险翻倍，且新冠相关预后更差。然而，当前学界对COVID-19与HIV之间交互作用的分子机制仍缺乏足够认知。本研究旨在阐明COVID-19与HIV之间的血液转录组交互调控机制，并为HIV合并COVID-19患者的治疗提供潜在候选药物。
研究方法：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）下载两组COVID-19数据集（GSE171110与GSE152418），分别包含54份全血样本与33份外周血单个核细胞样本；同时从GEO数据库获取HIV数据集（GSE37250），该数据集包含537份全血样本。随后，分别使用"Deseq2"包对合并的COVID-19数据集（GSE171110与GSE152418）筛选差异表达基因（differentially expressed genes, DEGs），使用"limma"包对HIV数据集（GSE37250）进行DEGs筛选。将两组DEGs取交集后得到共同DEGs，用于后续分析：包括京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析、基因本体（Gene Ontology, GO）功能富集分析、蛋白质-蛋白质相互作用（protein-protein interaction, PPI）分析、转录因子（transcription factor, TF）候选靶点筛选、微小RNA（microRNAs, miRNAs）候选靶点筛选以及候选药物筛选。
研究结果：本研究从合并的COVID-19数据集（GSE171110与GSE152418）中共筛选得到3213个DEGs，从GSE37250数据集中共筛选得到1718个DEGs。随后，通过COVID-19与HIV数据集的DEGs取交集，共获得394个共同DEGs。GO与KEGG富集分析结果显示，共同DEGs主要富集于染色体相关及细胞周期相关信号通路。基于共同DEGs，通过度算法筛选得到排名前十的核心基因（hub genes）：CCNA2、CCNB1、CDC20、TOP2A、AURKB、PLK1、BUB1B、KIF11、DLGAP5及RRM2。此外，按P值排名的前十种候选药物（LUCANTHONE、Dasatinib、etoposide、Enterolactone、troglitazone、testosterone、estradiol、calcitriol、resveratrol、tetradioxin）被筛选出来，其或可用于HIV合并COVID-19患者的治疗。
研究结论：本研究明确了与HIV感染者不良COVID-19预后相关的潜在分子靶点、信号通路、小分子化合物及候选生物标志物，可为HIV合并COVID-19患者的精准诊断与治疗提供理论依据。